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PasreQ

1. Introduction

Paper:

Scene Text Recognition with Permuted Autoregressive Sequence Models Darwin Bautista, Rowel Atienza ECCV, 2021

Using real datasets (real) and synthetic datsets (synth) for training respectively，and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets.

  • The real datasets include COCO-Text, RCTW17, Uber-Text, ArT, LSVT, MLT19, ReCTS, TextOCR and OpenVINO datasets.
  • The synthesis datasets include MJSynth and SynthText datasets.

the algorithm reproduction effect is as follows:

Training Dataset Model Backbone config Acc Download link
Synth ParseQ VIT rec_vit_parseq.yml 91.24% train model
Real ParseQ VIT rec_vit_parseq.yml 94.74% train model

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone"to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Tutorial. PaddleOCR modularizes the code, and training different recognition models only requires changing the configuration file.

Training

Specifically, after the data preparation is completed, the training can be started. The training command is as follows:

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# Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_vit_parseq.yml

# Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_vit_parseq.yml

Evaluation

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# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_vit_parseq.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

Prediction

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# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_vit_parseq.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the SAR text recognition training process is converted into an inference model. ( Model download link ), you can use the following command to convert:

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python3 tools/export_model.py -c configs/rec/rec_vit_parseq.yml -o Global.pretrained_model=./rec_vit_parseq_real/best_accuracy Global.save_inference_dir=./inference/rec_parseq

For SAR text recognition model inference, the following commands can be executed:

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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_parseq/" --rec_image_shape="3, 32, 128" --rec_algorithm="ParseQ" --rec_char_dict_path="ppocr/utils/dict/parseq_dict.txt" --max_text_length=25 --use_space_char=False

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@InProceedings{bautista2022parseq,
  title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
  author={Bautista, Darwin and Atienza, Rowel},
  booktitle={European Conference on Computer Vision},
  pages={178--196},
  month={10},
  year={2022},
  publisher={Springer Nature Switzerland},
  address={Cham},
  doi={10.1007/978-3-031-19815-1_11},
  url={https://doi.org/10.1007/978-3-031-19815-1_11}
}

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